{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(666)\n",
    "x = np.random.uniform(-3, 3, 100)\n",
    "X = x.reshape(-1, 1)\n",
    "y = 0.5 * x ** 2 + x + 2 + np.random.normal(0, 1, size = 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100, 1)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100,)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin_reg = LinearRegression()\n",
    "lin_reg.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predict = lin_reg.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118870e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x, y)\n",
    "plt.plot(x, y_predict, color='g')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.49537078118650091"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin_reg.score(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.0750025765636577"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "mean_squared_error(y, y_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0987392142417858"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "\n",
    "def PolynomialRegression(degree):\n",
    "    return Pipeline([\n",
    "        (\"poly\", PolynomialFeatures(degree=degree)),\n",
    "        (\"std_scaler\", StandardScaler()),\n",
    "        (\"lin_reg\", LinearRegression())\n",
    "    ])\n",
    "\n",
    "poly_reg = PolynomialRegression(degree = 2)\n",
    "poly_reg.fit(X, y)\n",
    "y_predict = poly_reg.predict(X)\n",
    "mean_squared_error(y, y_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1f977358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x, y)\n",
    "plt.plot(np.sort(x), y_predict[np.argsort(x)], color='g')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0508466763764153"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poly_reg10 = PolynomialRegression(degree = 10)\n",
    "poly_reg10.fit(X, y)\n",
    "y_predict10 = poly_reg10.predict(X)\n",
    "mean_squared_error(y, y_predict10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1f748a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x, y)\n",
    "plt.plot(np.sort(x), y_predict10[np.argsort(x)], color='g')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.68728961485464657"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poly_reg100 = PolynomialRegression(degree = 100)\n",
    "poly_reg100.fit(X, y)\n",
    "y_predict100 = poly_reg100.predict(X)\n",
    "mean_squared_error(y, y_predict100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1f3b04e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x, y)\n",
    "plt.plot(np.sort(x), y_predict100[np.argsort(x)], color='g')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_plot = np.linspace(-3, 3, 100)\n",
    "X_plot = x_plot.reshape(100, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_plot = poly_reg100.predict(X_plot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1f70afd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x, y)\n",
    "plt.plot(X_plot, y_plot, color='g')\n",
    "plt.axis([-3, 3, -1, 10])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
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